Association Between Genetic Traits for Immune-Mediated Diseases and Alzheimer Disease

Supplementary Online Content

Yokoyama JS, Wang Y, Schork AJ, et al; Alzheimer’s Disease Neuroimaging Initiative. Association between genetic traits for immune-mediated diseases and Alzheimer disease. JAMA Neurol. Published online April 18, 2016. doi:10.1001/jamaneurol.2016.0150.

eAppendix 1. List of Investigators eAppendix 2. Additional Information eReferences eFigure 1. Conditional quantile-quantile (Q-Q) plots after removing all SNPs located in the MHC (location 25652429 - 33368333) on chromosome 6. eFigure 2. Conditional quantile-quantile (Q-Q) plots of selected phenotypes as a function of Alzheimer’s Disease.

This supplementary material has been provided by the authors to give readers additional information about their work.

Downloaded From: https://jamanetwork.com/ on 09/26/2021 eAppendix 1. List of Investigators

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

ADNI Investigators:

Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Liu E, Green RC, Montine T, Petersen R, Aisen P, Gamst A, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Beckett L, Harvey D, Gamst A, Donohue M, Kornak J, Jack CR Jr, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Schuff N, Alexander G, DeCarli C, Jagust W, Bandy D, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Morris J, Cairns NJ, Taylor-Reinwald L, Trojanowki JQ, Shaw L, Lee VM, Korecka M, Toga AW, Crawford K, Neu S, Saykin AJ, Foroud TM, Potkin S, Shen L, Kachaturian Z, Frank R, Snyder PJ, Molchan S, Kaye J, Quinn J, Lind B, Dolen S, Schneider LS, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Petersen R, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Morris JC, Ances B, Carroll M, Leon S, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Mitsis E, Romirowsky A, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert M, Onyike C, Kielb S, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy PM, Petrella JR, Coleman RE, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail MS, Brand C, Mulnard RA, Thai G, Mc-Adams- Ortiz C, Womack K, Mathews D, Quiceno M, Diaz-Arrastia R, King R, Weiner M, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Apostolova L, Lu PH, Bartzokis G, Silverman DH, Graff-Radford NR, Parfitt F, Johnson H, Farlow MR, Hake AM, Matthews BR, Herring S, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Robin Hsiung GY, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Kerwin D, Mesulam MM, Lipowski K, Wu CK, Johnson N, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Frey M, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh M, Belden C, Jacobson S, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Bwayo SK, Lerner A, Hudson L, Ogrocki P, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Saykin AJ, Santulli RB, Schwartz ES, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Longmire CF, Spicer K, Finger E, Rachinsky I, Rogers J, Kertesz A, Drost D, Pomara N, Hernando R, Sarrael A, Schultz SK, Boles Ponto LL, Shim H, Smith KE, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Ashok Raj B.

The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The following is a list of the consortia members:

IGAP consortium:

Jean-Charles Lambert, Carla A Ibrahim-Verbaas, Denise Harold, Adam C Naj, Rebecca Sims, Céline Bellenguez, Gyungah Jun, Anita L DeStefano, Joshua C Bis, Gary W Beecham, Benjamin Grenier-Boley, Giancarlo Russo, Tricia A Thornton-Wells, Nicola Jones, Albert V Smith, Vincent Chouraki, Charlene Thomas, M Arfan Ikram, Diana Zelenika, Badri N Vardarajan, Yoichiro Kamatani, Chiao-Feng Lin, Amy Gerrish, Helena Schmidt, Brian Kunkle, Melanie L Dunstan, Agustin Ruiz, Marie-Thérèse Bihoreau, Seung-Hoan Choi, Christiane Reitz, Florence Pasquier, Paul Hollingworth, Alfredo Ramirez, Olivier Hanon, Annette L Fitzpatrick, Joseph D Buxbaum, Dominique Campion, Paul K Crane, Clinton Baldwin, Tim Becker, Vilmundur Gudnason, Carlos Cruchaga, David

Downloaded From: https://jamanetwork.com/ on 09/26/2021 Craig, Najaf Amin, Claudine Berr, Oscar L Lopez, Philip L De Jager, Vincent Deramecourt, Janet A Johnston, Denis Evans, Simon Lovestone, Luc Letenneur, Francisco J Morón, David C Rubinsztein, Gudny Eiriksdottir, Kristel Sleegers, Alison M Goate, Nathalie Fiévet, Matthew J Huentelman, Michael Gill, Kristelle Brown, M Ilyas Kamboh, Lina Keller, Pascale Barberger-Gateau, Bernadette McGuinness, Eric B Larson, Robert Green, Amanda J Myers, Carole Dufouil, Stephen Todd, David Wallon, Seth Love, Ekaterina Rogaeva, John Gallacher, Peter St George-Hyslop, Jordi Clarimon, Alberto Lleo, Anthony Bayer, Debby W Tsuang, Lei Yu, Magda Tsolaki, Paola Bossù, Gianfranco Spalletta, Petroula Proitsi, John Collinge, Sandro Sorbi, Florentino Sanchez-Garcia, Nick C Fox, John Hardy, Maria Candida Deniz Naranjo, Paolo Bosco, Robert Clarke, Carol Brayne, Daniela Galimberti, Michelangelo Mancuso, Fiona Matthews, European Alzheimer’s disease Initiative (EADI), Genetic and Environmental Risk in Alzheimer’s Disease (GERAD), Alzheimer’s Disease Genetic Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), Susanne Moebus, Patrizia Mecocci, Maria Del Zompo, Wolfgang Maier, Harald Hampel, Alberto Pilotto, Maria Bullido, Francesco Panza, Paolo Caffarra, Benedetta Nacmias, John R Gilbert, Manuel Mayhaus, Lars Lannfelt, Hakon Hakonarson, Sabrina Pichler, Minerva M Carrasquillo, Martin Ingelsson, Duane Beekly, Victoria Alvarez, Fanggeng Zou, Otto Valladares, Steven G Younkin, Eliecer Coto,Kara L Hamilton-Nelson,Wei Gu, Cristina Razquin,Pau Pastor, Ignacio Mateo, Michael J Owen, Kelley M Faber, Palmi V Jonsson, Onofre Combarros, Michael C O’Donovan, Laura B Cantwell, Hilkka Soininen, Deborah Blacker, Simon Mead, Thomas H Mosley, Jr, David A Bennett, Tamara B Harris, Laura Fratiglioni, Clive Holmes, Renee F A G de Bruijn, Peter Passmore, Thomas J Montine, Karolien Bettens, Jerome I Rotter, Alexis Brice, Kevin Morgan, Tatiana M Foroud, Walter A Kukull, Didier Hannequin, John F Powell, Michael A Nalls, Karen Ritchie, Kathryn L Lunetta, John S K Kauwe, Eric Boerwinkle, Matthias Riemenschneider, Mercè Boada, Mikko Hiltunen, Eden R Martin, Reinhold Schmidt, Dan Rujescu,Li-san Wang, Jean-François Dartigues, Richard Mayeux, Christophe Tzourio,Albert Hofman, Markus M Nöthen, Caroline Graff, Bruce M Psaty, Lesley Jones, Jonathan L Haines, Peter A Holmans, Mark Lathrop, Margaret A Pericak-Vance, Lenore J Launer, Lindsay A Farrer, Cornelia M van Duijn, Christine Van Broeckhoven, Valentina Moskvina, Sudha Seshadri, Julie Williams, Gerard D Schellenberg, and Philippe Amouyel

DemGene Investigators: Ole A. Andreassen, Ingun Dina Ulstein, Dag Aarsland, Tormod Fladby, Linda R. White, Sigrid B. Sando, Arvid Rongve, Aree Witoelar, Srdjan Djurovic, Ina Selseth Almdahl, Fred Andersen, Nenad Bogdanovic, Anne Brækhus, Knut Engedal, Ingvild Saltvedt, Eystein Stordal

Inflammation Working Group: Abbas Dehghan, Josée Dupuis, Maja Barbalic, Joshua C Bis, Gudny Eiriksdottir, Chen Lu, Niina Pellikka, Henri Wallaschofski, Johannes Kettunen, Peter Henneman, Jens Baumert, David P Strachan, Christian Fuchsberger, Veronique Vitart, James F Wilson, Guillaume Paré, Silvia Naitza, Megan E Rudock, Ida Surakka, Eco JC de Geus, Behrooz Z Alizadeh, Jack Guralnik MD, Alan Shuldiner, Toshiko Tanaka, Robert YL Zee, Renate B Schnabel, Vijay Nambi, Maryam Kavousi, Samuli Ripatti, Matthias Nauck, Nicholas L Smith, Albert V Smith, Jouko Sundvall, Paul Scheet, Yongmei Liu, Aimo Ruokonen, Lynda M Rose, Martin G Larson, Ron C Hoogeveen, Nelson B Freimer, Alexander Teumer, Dipl-Math, Russell P Tracy, Lenore J Launer, Julie E Buring, Jennifer F Yamamoto, Aaron R Folsom, Eric JG Sijbrands, James Pankow, Paul Elliott, John F Keaney, Wei Sun, Antti-Pekka Sarin, João D Fontes, Sunita Badola, Brad C Astor, Albert Hofman, Anneli Pouta, Karl Werda, Karin H Greiser, Oliver Kuss, Henriette E Meyer zu Schwabedissen, Joachim Thiery, Yalda Jamshidi, Ilja M Nolte, Nicole Soranzo, Timothy D Spector, Henry Völzke, Alexander N Parker, Thor Aspelund, David Bates, Lauren Young, Kim Tsui, David S Siscovick, Xiuqing Guo, Jerome I Rotter, Manuela Uda, David Schlessinger, Igor Rudan, Andrew A Hicks, Brenda W Penninx, Barbara Thorand, Christian Gieger, Joe Coresh, Gonneke Willemsen, Tamara B Harris, Andre G Uitterlinden, Marjo-Riitta Järvelin, Kenneth Rice, Dörte Radke, Veikko Salomaa, Ko Willems van Dijk, Eric Boerwinkle, Ramachandran S Vasan, Luigi Ferrucci, Quince D Gibson, Stefania Bandinelli, Harold Snieder, Dorret I Boomsma, Xiangjun Xiao, Harry Campbell, Caroline Hayward, Peter P Pramstaller, Cornelia M van Duijn, Leena Peltonen, Bruce M Psaty, Vilmundur Gudnason, Paul M Ridker, Georg Homuth, Wolfgang Koenig, Christie M Ballantyne, Jacqueline CM Witteman, Emelia J Benjamin, Markus Perola, Daniel I Chasman.

Downloaded From: https://jamanetwork.com/ on 09/26/2021 eAppendix 2. Additional Information

IGAP Cohort International Genomics of Alzheimer's Project (IGAP) is a large two-stage study based upon genome-wide association studies (GWAS) on individuals of European ancestry. In stage 1, IGAP used genotyped and imputed data on 7,055,881 single nucleotide polymorphisms (SNPs) to meta-analyse four previously-published GWAS datasets consisting of 17,008 Alzheimer's disease cases and 37,154 controls (The European Alzheimer's disease Initiative – EADI the Alzheimer Disease Genetics Consortium – ADGC The Cohorts for Heart and Aging Research in Genomic Epidemiology consortium – CHARGE The Genetic and Environmental Risk in AD consortium – GERAD). In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. Finally, a meta-analysis was performed combining results from stages 1 & 2. In this study, we focused on the stage 1 IGAP SNPs.

ADNI Cohort The association between genotype and longitudinal clinical decline (ADAS-Cog) was assessed using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. The initial goal of ADNI was to recruit 800 subjects but ADNI has been followed by ADNI-GO and ADNI-2. To date these three protocols have recruited over 1500 adults, ages 55 to 90, to participate in the research, consisting of cognitively normal older individuals, people with early or late MCI, and people with early AD. The follow up duration of each group is specified in the protocols for ADNI-1, ADNI-2 and ADNI-GO. Subjects originally recruited for ADNI-1 and ADNI-GO had the option to be followed in ADNI-2. For up-to-date in formation, see www.adni-info.org.

Each participant was formally evaluated using eligibility criteria that are described in detail elsewhere (http://www.adni-info.org/index.php?option=com_content&task=view&id=9&Itemid=43). The institutional review boards of all participating institutions approved the procedures for this study. Written informed consent was obtained from all participants or surrogates. Experienced clinicians conducted independent semi-structured interviews with the participant and a knowledgeable collateral source that included a health history, neurological examination, and a comprehensive neuropsychological battery. We selected participants from the ADNI database if they were clinically diagnosed at baseline as cognitively normal (n = 196), amnestic mild cognitive impairment (MCI) (n = 355) or probable AD (n = 172).

Supplemental funding sources IGAP: The i–Select chips was funded by the French National Foundation on Alzheimer's disease and related disorders. EADI was supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical Research Council (Grant n° 503480), Alzheimer's Research UK (Grant n° 503176), the Wellcome Trust (Grant n° 082604/2/07/Z) and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant n° 01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by the NIH/NIA grant R01 AG033193 and the NIA AG081220 and AGES contract N01–AG–12100, the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer's Association grant ADGC–10–196728.

Downloaded From: https://jamanetwork.com/ on 09/26/2021 Conditional Q-Q plots Q-Q plots compare a nominal probability distribution against an empirical distribution. In the presence of all null relationships, nominal p-values form a straight line on a Q-Q plot when plotted against the empirical distribution. For AD, CD, UC, RA, T1D, CeD, and PSOR SNPs and for each categorical subset (strata), -log nominal p-values 10 were plotted against -log empirical p-values (conditional Q-Q plots, see Figure 1). Deflections of the observed 10 distribution from the projected null line reflect increased tail probabilities in the distribution of test statistics (z- scores) and consequently an over-abundance of low p-values compared to that expected by chance (enrichment). Under large-scale testing paradigms, such as GWAS, quantitative estimates of likely true associations can be estimated from the distributions of summary statistics (1, 2). One common method for visualizing the enrichment of statistical association relative to that expected under the global null hypothesis is through Q-Q plots of nominal p- values obtained from GWAS summary statistics. The usual Q-Q curve has as the y-ordinate the nominal p-value, denoted by “p”, and as the x-ordinate the corresponding value of the empirical cdf, denoted by “q”. Under the global null hypothesis the theoretical distribution is uniform on the interval [0,1]. As is common in GWAS, we instead plot -log10 p against -log10 q to emphasize tail probabilities of the theoretical and empirical distributions. Therefore, genetic enrichment results in a leftward shift in the Q-Q curve, corresponding to a larger fraction of SNPs with nominal -log10 p-value greater than or equal to a given threshold. Conditional Q-Q plots are constructed by creating subsets of SNPs based on levels of an auxiliary measure for each SNP, and computing Q-Q plots separately for each level. If SNP enrichment is captured by variation in the auxiliary measure, this is expressed as successive leftward deflections in a conditional Q-Q plot as levels of the auxiliary measure increase. We constructed conditional Q-Q plots of empirical quantiles of nominal –log10(p) values for SNP association with AD for all SNPs, and for subsets (strata) of SNPs determined by the nominal p-values of their association with CD, UC, RA, T1D, CeD, and PSOR. Specifically, we computed the empirical cumulative distribution of nominal p-values for a given phenotype for all SNPs and for SNPs with significance levels below the indicated cut-offs for the other phenotypes (–log10(p) ≥ 0, –log10(p) ≥ 1, –log10(p) ≥2 corresponding to p < 1, p < 0.1, p < 0.01 respectively). The nominal p-values (–log10(p)) are plotted on the y-axis, and the empirical quantiles (– log10(q), where q=1-cdf(p)) are plotted on the x-axis (Figure 1). To assess for polygenic effects below the standard GWAS significance threshold, we focused the conditional Q-Q plots on SNPs with nominal –log10(p) < 7.3 (corresponding to p > 5x10-8).

Genomic Control The empirical null distribution in GWAS is affected by global variance inflation due to population stratification and cryptic relatedness (3) and deflation due to over-correction of test statistics for polygenic traits by standard genomic control methods (4). We applied a control method leveraging only intergenic SNPs, which are likely depleted for true associations (5). First, we annotated the SNPs to genic (5’UTR, exon, intron, 3’UTR) and intergenic regions using information from the 1KGP. We used intergenic SNPs because their relative depletion of associations suggests that they provide a robust estimate of true null effects and thus seem a better category for genomic control than all SNPs. We converted all p-values to z-scores and for all phenotypes we estimated the genomic inflation factor λGC for intergenic SNPs. We computed the inflation factor, λGC as the median z-score squared divided by the expected median of a chi-square distribution with one degree of freedom and divided all test statistics by λGC.

Conditional True Discovery Rate (TDR) Enrichment seen in the fold enrichment plots can be directly interpreted in terms of TDR (equivalent to one minus the False Discovery Rate (FDR)) (6). We applied the conditional FDR method (7), previously used for enrichment of GWAS based on linkage information (8). Specifically, for a given p-value cutoff, the FDR is defined as FDR(p) = π0F0(p) / F(p), [1] where π0 is the proportion of null SNPs, F0 is the null cdf, and F is the cdf of all SNPs, both null and non-null. Under the null hypothesis, F0 is the cdf of the uniform distribution on the unit interval [0,1], so that Eq. [1] reduces to FDR(p) = π0p / F(p), [2] The cdf F can be estimated by the empirical cdf q =Np / N, where Np is the number of SNPs with p-values less than or equal to p, and N is the total number of SNPs. Replacing F by q in Eq. [2], we get Estimated FDR(p) = π0p / q, [3] which is biased upwards as an estimate of the FDR (9). Replacing π0 in Equation [3] with unity gives an estimated FDR that is further biased upward;

Downloaded From: https://jamanetwork.com/ on 09/26/2021 q* = p/q [4] If π0 is close to one, as is likely true for most GWAS, the increase in bias from Eq. [3] is minimal. The quantity 1 – p/q, is therefore biased downward, and hence is a conservative estimate of the TDR. Referring to the formulation of the Q-Q plots, we see that q* is equivalent to the nominal p-value divided by the empirical quantile, as defined earlier. Given the -log10 of the Q-Q plots we can easily obtain -log10(q*) = log10(q) – log10(p) [5] demonstrating that the (conservatively) estimated FDR is directly related to the horizontal shift of the curves in the conditional Q-Q plots from the expected line x = y, with a larger shift corresponding to a smaller FDR, as illustrated in Figure 1. As before, the estimated TDR can be obtained as 1-FDR.

Conjunction statistics – test of association with both phenotypes We defined the conjunction statistics (denoted as FDR Trait1 & Trait2) as the maximum of the conditional FDR in both directions, i.e. FDR Trait1 & Trait2 = max(FDR Trait1 | Trait2, FDR Trait2 | Trait1) based on the combination of p-value for the SNP in AD and the associated immune-mediated disease (e.g. CD), by interpolation into a bidirectional 2-D look-up table (1, 2). The conjunction statistic allows for identification of SNPs that are associated with both phenotypes, which minimizes the effect of a single phenotype driving the common association signal. Table 1 lists all SNPs with conjunction FDR < 0.05 (-log10(FDR) > 1.3) with AD and any of the immune-mediated diseases considered after removing all SNPs with r2 > 0.2 based on 1KGP linkage disequilibrium (LD) (pruning).

Conjunction FDR Manhattan plots To illustrate the localization of the genetic markers associated with AD given CD, UC, RA, T1D, CeD, and PSOR we used a ‘Conjunction FDR Manhattan plot’, plotting all SNPs within an LD block in relation to their chromosomal location. As illustrated in Figure 2 within the main manuscript, the large points represent the SNPs with FDR < 0.05, whereas the small points represent the non-significant SNPs. All SNPs before ‘pruning’ (removing all SNPs with r2 > 0.2 based on 1KGP LD structure) are shown. The strongest signal in each LD block is illustrated with a black line around the circles. This was identified by ranking all SNPs in increasing order, based on the conditional FDR value for AD, and then removing SNPs in LD r2 > 0.2 with any higher ranked SNP. Thus, the selected locus was the most significantly associated with AD in each LD block (Figure 2).

Influence of the MHC region on genetic enrichment The major histocompatibility complex (MHC) is recognized as an important factor in the pathology of immune- mediated diseases. To examine the effect of the MHC on enrichment in AD SNPs, we evaluated the conditional Q-Q plots after removing the SNPs in the MHC region. We removed all SNPs located in the MHC (location 25652429 - 33368333) on chromosome 6 and all SNPs in LD with these MHC SNPs (r2 > 0.2) and re-examined the AD Q-Q plots conditional on the immune-mediated traits.

Downloaded From: https://jamanetwork.com/ on 09/26/2021 eReferences 1. Efron B (2010) Large-scale inference : empirical Bayes methods for estimation, testing, and prediction (Cambridge University Press, New York). 2. Schweder T, Spjøtvoll E (1982) Plots of p-values to evaluate many tests simultaneously. Biometrika 69(3):493–502. 3. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55(4):997–1004. 4. Yang J, et al. (2011) Genomic inflation factors under polygenic inheritance. Eur J Hum Genet 19(7):807– 812. 5. Schork AJ, et al. (2013) All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet 9(4):e1003449. 6. Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B 57(1):289 – 300. 7. Sun L, Craiu R V, Paterson AD, Bull SB (2006) Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genet Epidemiol 30(6):519–30. 8. Yoo YJ, Pinnaduwage D, Waggott D, Bull SB, Sun L (2009) Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results. BMC Proc 3(Suppl 7):S103. 9. Efron B (2007) Size, power and false discovery rates. Ann Stat 35(4):1351–1377.

Downloaded From: https://jamanetwork.com/ on 09/26/2021 eFigure 1. Conditional quantile-quantile (Q-Q) plots after removing all SNPs located in the MHC (location 25652429 - 33368333) on chromosome 6.

We plot empirical -log10 p-values versus nominal -log10 p-values (corrected for inflation) in Alzheimer’s disease (AD) below the standard GWAS threshold of p < 5x10-8 as a function of significance of association with Crohn’s disease (CD) (panel A), ulcerative colitis (UC) (panel B), Type 1 Diabetes (T1D) (panel C), rheumatoid arthritis (RA) (panel D), Celiac disease (CeD) (panel E), and psoriasis (PSOR) (panel F) at the level of -log10(p) ≥ 0, -log10(p) ≥ 1, -log10(p) ≥ 2 corresponding to p ≤ 1, p ≤ 0.1, p ≤ 0.01, respectively. Blue line indicates all SNPs.

Downloaded From: https://jamanetwork.com/ on 09/26/2021 eFigure 2. Conditional quantile-quantile (Q-Q) plots of selected phenotypes as a function of Alzheimer’s Disease.

Conditional quantile-quantile (Q-Q) plots of empirical -log10 p-values versus nominal -log10 p- values (corrected for inflation) in Crohn’s disease (CD) (panel A), ulcerative colitis (UC) (panel B), Type 1 Diabetes (T1D) (panel C), rheumatoid arthritis (RA) (panel D), Celiac disease (CeD) (panel E), and psoriasis (PSOR) (panel F) as a function of Alzheimer’s disease (AD) below the standard GWAS threshold of p < 5x10-8 as a function of significance of association with at the level of -log10(p) ≥ 0, -log10(p) ≥ 1, -log10(p) ≥ 2 corresponding to p ≤ 1, p ≤ 0.1, p ≤ 0.01, respectively. Blue line indicates all SNPs.

Downloaded From: https://jamanetwork.com/ on 09/26/2021